As one of the fundamentals of metabolic engineering, metabolic flux analysis (MFA) has been widely used to quantify intracellular metabolic fluxes in wild type and engineered mutant type over the past three decades. It is an experimental fluxomics widely used to examine production and consumption rates of metabolites in a biological system.
Combined with stable isotopes and sensitive detection of advanced analytical methods, metabolic flux pathway analysis offers mechanism details. With the help of stable isotopes like 2H. 13C and 15N, flux analysis offers unique opportunity for establishing metabolic routes unambiguously and determining the generation and consumption rates of metabolites. Cells or animal models are supplied with 2H, 13C or 15N isotope-labeled substrates and the formed isotope-labeled metabolites can be analyzed to derive detailed information about pathways and fluxes. Because flux analysis can offer detailed information about metabolomics pathway, it is of great interest to do metabolite flux analysis on different pathways.
Variations in metabolic pathway fluxes that result from genetic or environmental effects can be quantified through MFA. The MFA result will give clues to the regulation of metabolic pathways and may provides insights into new targets for further metabolic engineering. Various methods of MFA are available, such as stoichiometric MFA, dynamic metabolic flux analysis, isotopic non-stationary metabolic flux analysis. Each method has its own advantages and limitations. Among these approaches, 13C-based metabolic flux analysis has been developed as a standard tool and has been widely used for quantitative pathway characterization of diverse biological systems. To implement 13C-based metabolic flux analysis properly, it is of great importance to understand the underlying mathematical and computational modeling fundamentals and analytical measurements.
If the various metabolic pathways inside the cell are regarded as highways, MFA is similar to drafting a traffic report describing the flow across these highways and how they change in response to detour or roadblock. By comparing flux maps obtained under different experimental conditions or in the presence of targeted genetic mutation provides a functional readout on the comprehensive impact of these perturbations have on cell metabolism, which is crucial for understanding how metabolic pathways are regulated in normal cells—and how they become out of order in diseased cells. Furthermore, it enables us to point out crucial metabolic points that can be manipulated to enhance production rates or to restore unhealthy cells to normal metabolic function.
Herein, Creative proteomics develops a novel metabolic flux analysis platform. What is important is that this platform allows for the determination of control structure and regulation network of the metabolic pathway. Our approach is composed of several integrated quantitative methodologies, including a detailed computational dynamic model accounting for regulatory interactions. This analytical platform is widely applicable to any metabolic system that can be depicted by kinetic modeling. The advantage of our platform is that it includes regulatory information crucial for understanding dynamic systems. For example, in response to energy demand, heart is constantly changing. The platform is a good choice to account for time-dependent behavior of heart function. The application of this platform could also give insight into mechanisms underlying cardiac contractile disorder.